In order to quantitatively evaluate the enhancement performance, we apply two simple threshold methods to compute the binary maps: global Otsu's threshold [1]} and local Sauvola's threshold [2]} both on the original and the enhanced images computed by the proposed methods, similar as [3]}.
Table REF  and REF  show the increased performance of the binarization maps computed on the original images (OI) and the enhanced images by the proposed SR and RR methods on the DIBCO 2013 and MCS datasets, respectively.
From these two tables we can see that the significant improvement is achieved using the proposed deep enhancement methods as a preprocessing step.
Especially, the Sauvola's threshold with the SR methods provides best performance on the two data sets, because the background of the whole image does not uniform since our model works on small patches.
